Many industries benefit from information technologies (IT) that facilitate collecting and drawing conclusions from aggregated data. Generally, the larger the data set, the better the conclusions that can be drawn from the data. However, such a task is complex, time-consuming and costly to perform on a large-scale.
Moreover, some industries face unique IT obstacles that further increase the difficulties of large-scale data acquisition and management. For instance, because health histories generally are stored in private, non-uniform databases, assembling this data on a large-scale would be extremely expensive. By way of another example, collecting and aggregating drug safety information is an important but challenging IT task. To address the potential for harmful drug effects, many countries establish government agencies to approve a pharmaceutical or medical device product before it can be sold to the public. These agencies usually require proof of efficacy and of an acceptable safety profile before the pharmaceuticals and medical devices are approved for sale. Typically the proof is obtained by conducting clinical trials on selected populations. These trials usually take many months and are quite expensive to conduct. In addition, some countries have post-market surveillance mechanisms in place, such as mandatory and voluntary adverse event reporting. However, delays inherent in the current systems have resulted in medications and devices with unacceptable risks remaining on the market during the time the data is being collected and aggregated.